There are several near earth orbiting commercial satellites that can provide images of structures or targets on the ground. For applications that rely on these images, it is important to accurately register the images to respective coordinates on the ground.
For satellite-generated images of earth-based targets, one known source of misregistration is referred to as velocity aberration. Velocity aberration can arise in an optical system with a sufficiently large velocity relative to the point being imaged. A typical velocity of a near earth orbiting commercial satellite can be on the order of 7.5 kilometers per second, with respect to a location on the earth directly beneath the satellite. This velocity is large enough to produce a registration error of several detector pixels at the satellite-based camera.
The correction for velocity aberration is generally well-known. However, it is generally challenging to calculate error terms associated with the correction. These error terms estimate the confidence level, or reliability, of the velocity aberration correction.
Historically, error calculation for velocity aberration correction has been treated statistically with a Monte Carlo analysis. In general, these Monte Carlo analyses can be time-consuming and computationally expensive. In order to produce statistically significant results, a Monte Carlo analysis can require that a large number of simulated cases be executed and analyzed, which can be difficult or impossible due to the limitations of computational resources and processing time requirements. As a result, error estimation for velocity aberration correction can be lacking.